The present invention relates generally to diagnostic support tools and more particularly exploiting existing products such as fault trees for diagnostic support.
Fault trees are graphical representations of system failures. They show how failures of components contribute to system failures. The trees consists of nodes representing component failures and nodes representing functional failures of systems. They are connected by means of logic gates. Fault trees are used for quantitative reliability and fault analysis of high value or high risk systems. Bayesian networks (or belief networks) are a much more general representation of the system. They can be used not only to compute reliability and perform fault analysis, but also to assist in system diagnosis.
By exploiting existing products, specifically fault trees containing embedded or separate reliability data and observation databases, advanced high fidelity diagnostic tools based on Bayesian networks can be developed quickly and at low cost, without requiring an independent development effort. Currently such diagnostic products are typically not delivered on many systems (e.g., satellite programs) because of the prohibitive cost. However, the invention disclosed herein is not limited in application to satellite systems.
There is thus a need in the art for a system that converts the fault trees automatically into a probabilistic graphical representation called a Bayesian network or a belief network and enables a more inexpensive delivery of system diagnostics tools, allowing early and accurate detection of failures and a more rapid return to normal operation.